HAMSTER: using search clicklogs for schema and taxonomy matching

  • Authors:

  • Arnab Nandi;Philip A. Bernstein

  • Affiliations:

  • University of Michigan, Ann Arbor;Microsoft Research

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

We address the problem of unsupervised matching of schema information from a large number of data sources into the schema of a data warehouse. The matching process is the first step of a framework to integrate data feeds from third-party data providers into a structured-search engine's data warehouse. Our experiments show that traditional schema-based and instance-based schema matching methods fall short. We propose a new technique based on the search engine's clicklogs. Two schema elements are matched if the distribution of keyword queries that cause click-throughs on their instances are similar. We present experiments on large commercial datasets that show the new technique has much better accuracy than traditional techniques.